What Is Data Classification? The 5 Step Process & Best Practices for Classifying Data

You can extract meaningful insights from data only when you can manage, protect and understand it. 

With data generated at unprecedented rates and a large proportion of that data in raw unstructured formats, the process of understanding and extracting insights is becoming more daunting by the day.

Thankfully, to make sense of all that raw data, we can start to group similar types of data and begin to describe them. This process is known as data classification, and it’s incredibly important in data management. 

Let’s talk about why:

What is data classification?

Data classification refers to the process of organizing data in groups based on their attributes and characteristics, and then assigning class labels that describe a set of attributes that hold true for the corresponding data sets. We can consider it one part of an overarching data management practice.

The goal is to provide meaningful class attributes to raw unstructured information. This allows users to extract insights from a dataset while following data handling and security best practices.

(This article was written in collaboration with Muhammad Raza.)

The classification process involves a lot of analysis, and it isn’t limited to the data itself. You’ll want to understand…

  • The data’s content itself
  • The level(s) of sensitivity of different datasets
  • Environmental and technical attributes, like whether all or some data needs to transit
  • The overall importances or value of the dataset to you
  • Any regulatory compliance that applies to that data

When does data classification take place?

From the moment data is created, it starts on its way through a data lifecycle. Data lifecycle management (DLM) can change from business to business, but on its face, DLM is a set of established protocols for:

  • Data acquisition
  • Access
  • Data use
  • Data deletion

Each facet of a DLM protocol is designed to safeguard data and achieve regulatory compliance. Here’s how classification fits into that lifecycle:

Data lifecycle vs data classification

While data lifecycle approaches can differ greatly across the industry, for our purposes, the data lifecycle generally consists of these five phases:

  1. Collection & preprocessing
  2. Storage
  3. Processing
  4. Sharing & usage
  5. Deletion & archiving

The early stages of the data life cycle involve data preprocessing and transformation. At this step, the data is transformed to fit familiar formats for the users and systems that will process and transform that data into actionable insights. 

Data preprocessing may also take place at a later stage, such as following a schema-on-write mechanism where raw data is classified and assigned structure attributes. This would occur before analytics or machine learning processing.

Data classification generally takes place in one of these lifecycle phases:

  • Collection & preprocessing
  • Storage
  • Sharing & usage

Classification may be used at the data preprocessing stage, where raw data undergoes a classification process for schema-on-read database storage. 

Alternatively, data classification can be an outcome of analytics or AI activities — machine learning based data classification can be used to uncover insights, classifying an email as spam based on text and environment attributes of the communication.

The data classification process 

While embedded in the data lifecycle process, data classification consists of its own set of steps. Here’s what that process looks like: 

1. Collect the data

The first step of data classification often overlaps with the data aggregation phase of a typical data lifecycle management framework. At this step of the data classification process, users collect raw data based on attributes and parameters that may be useful for classification at a later stage.

2. Define classification levels

Organizations outline the business, technical, security, compliance and privacy objectives and how these apply to their data assets. 

For instance, HIPAA guidelines may apply to personally identifiable data collected by your organization. This data may be marked as sensitive and classified as subject to HIPAA privacy guidelines, and therefore, must be anonymized before further processing by third-party analytics tools.

This is also the step when you should identify who owns the data. The organization will often identify and involve data owners in the data classification process, tapping their expertise to accurately categorize data.

3. Categorize the data

This phase focuses on defining patterns and criteria which allow users to classify data assets. Data owners, in collaboration with IT and security teams, assess the content, context and potential impact of each data asset to determine its appropriate classification level. The categorization process may involve the following:

  • Interviews
  • Documentation reviews
  • Automated scanning tools 
  • Classification workflows

After moving through this process, the resulting guidelines ensure that data is managed in accordance with its sensitivity, and that data retention and disposal policies align with regulatory requirements and organizational best practices.

While placing all this data in predefined classification levels, teams will need to consider who should have access to each of these levels. This is why a flexible identity and access management approach, and a scalable data platform, is required for users to access raw data and create multiple class versions corresponding to data assets containing overlapping attributes.

In some cases, this may be in the form of more traditional role-based access control (RBAC), but in recent years many organizations turn to attribute-based access control (ABAC), as it affords them significant control over granular parameters. 

4. Apply security controls and appropriate monitoring

Once the data assets are classified, appropriate security controls and protection mechanisms are applied based on the assigned classification level. Levels generally include:

  • Low sensitivity data assets, such as press releases and public-facing content, may not require additional security measures
  • Medium sensitivity level classification outcomes such as IT network performance may require some security measures as they can expose the network to potential security risk
  • Highly sensitive data may include confidential information, IP-protected insights, financial and personally identifiable information

5. Review, updates and training

Data classification is an ongoing process that requires regular review and updates. As data evolves, new data assets are created and existing data may change in sensitivity. The organization should conduct periodic reviews of data classifications to ensure their accuracy and relevance, making adjustments as needed.

It's important that organizations continuously iterate on classification workflows, as this assists in improved data security and efficient data preprocessing, all of which supports data pipeline efficiency

Reviewing analysis outcomes and understanding the insights revealed using classified data assets can be used to improve business processes and outcomes. It’s also important to understand new risks and threat vectors incurred during the data classification process, as you can adjust security and IAM controls to account for newfound risks.

Alongside periodic review, effective communication and training are vital to ensure that all employees and stakeholders understand the data classification scheme, its importance, and their roles in implementing it. Training sessions and awareness programs help reinforce data protection practices and reinforce the significance of data security throughout the organization.

Summarizing data classification

Data classification is an essential process for modern data management. As data volumes continue to soar and a significant portion remains unstructured, understanding and extracting meaningful insights becomes increasingly challenging.

Data classification allows organizations to categorize and describe similar data types based on attributes, enabling organizations to acquire key insights while adhering to data handling and security best practices. 

By applying appropriate security controls, safeguarding data, and achieving regulatory compliance throughout its lifecycle, organizations can ensure data protection and make informed decisions. Engaging data owners and experts in the classification process, implementing attribute-based access control, and conducting regular reviews and training are key to a successful data classification strategy, empowering organizations to thrive in a data-driven world.

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Tyler York
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Tyler York

Tyler York is a writer, tech nerd and part of the growth marketing team at Splunk. Armed with an English degree, and a lifetime appointment as his family's IT contact, Tyler is interested in all the ways tech can help us — and even frustrate us.